Integration of Fault Analysis and Interlock Controller Synthesis for Batch Processes

Integration amongst various decision-making processes, such as planning, design, and operation is necessary to dynamic and flexible batch production. To achieve a batch production integration, utilization of common models used for various decision-making processes is an effective approach. From this point of view, a batch system common model as described by a Petri net is proposed. In this article, a fault diagnosis technique for batch processes is presented using information about fault propagation and the possibilities of integration of fault analysis and controller synthesis are discussed on the basis of the Petri net based common models.

Studies on Composting Spent Coffee Grounds by <i>Aspergillus sp</i>and <i>Aspergillus sp</i>in Aerobic Static Batch Temperature Control

Spent Coffee Ground (SCG) is characterized by high organic content, in the form of insoluble polysaccharides bound and phenol compounds. Phenol compounds are toxic to nature and are a cause of environmental pollution. Composting method of this study is aerobic static batch composting with temperature control with adding activators of some fungi such as Aspergillus sp, and Penicillium sp. The purpose of the research is to fill the research gap from previous studies of spent coffee grounds compost, which requires a long time in composting, so that if it is used directly on the soil and plants, the positive effect also requires a long time. The result of composting for 28 days with this method is that mature compost has black crumb and normal pH, with characteristics of C/N ratio below 10: C1 (7.06), C2 (6.99). This value is far from the control with a C/N ratio of 8.33. Decompose rate of macromolecule are above 40% for lignin and 70% for cellulose. Implementation of compost in radish plants, resulting Germination Index above 80% which indicates that the compost is ripe: control (92.39%), C1 (183.88%), C2 (191.86%). The results of the analysis with FTIR also showed that the compost was mature and stable, and rich in minerals. So, it can be concluded that this composting method can speed up composting time and optimize the results of compost produced.

Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a ＂global＂ nonlinear long-range prediction model through the fuzzy conjunction of a number of ＂local＂ linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.

2D multi-model general predictive iterative learning control for semi-batch reactor with multiple reactions

Batch to batch temperature control of a semi-batch chemical reactor with heating/cooling system was discussed in this study. Without extensive modeling investigations, a two-dimensional(2D) general predictive iterative learning control(2D-MGPILC) strategy based on the multi-model with time-varying weights was introduced for optimizing the tracking performance of desired temperature profile. This strategy was modeled based on an iterative learning control(ILC) algorithm for a 2D system and designed in the generalized predictive control(GPC) framework. Firstly, a multi-model structure with time-varying weights was developed to describe the complex operation of a general semi-batch reactor. Secondly, the 2 D-MGPILC algorithm was proposed to optimize simultaneously the dynamic performance along the time and batch axes. Finally, simulation for the controller design of a semi-batch reactor with multiple reactions was involved to demonstrate that the satisfactory performance could be achieved despite of the repetitive or non-repetitive disturbances.

Batch Process Modelling and Optimal Control Based on Neural Network Model

This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.

基于PCS7的BATCH控制在生物制药单抗生产中的应用

随着生物制药的发展,单抗药物的生产越来越朝着系统化、集成化、数字化的方向发展。针对单抗药物复杂的工艺生产过程和配方多变的生产方式。本文采用西门子PCS7全集成DCS系统,利用符合ISA-S88标准的批量控制软件和配方管理,研究单抗控制方案和第三方设备集成方案,设计出适用于单抗生产的批次控制DCS系统,满足其生产工艺、参数多变的柔性化需求和数据完整性要求。

Re-Testing in Batch Testing Model Based on Quality Control Process for Proportion Estimation

The quality of products manufactured or procured by organizations is an important aspect of their survival in the global market. The quality control processes put in place by organizations can be resource-intensive but substantial savings can be realized by using acceptance sampling in conjunction with batch testing. This paper considers the batch testing model based on the quality control process where batches that test positive are re-tested. The results show that re-testing greatly improves the efficiency over one stage batch testing based on quality control. This is observed using Asymptotic Relative Efficiency (ARE), where for values of p computed ARE > 1 implying that our estimator has a smaller variance than the one-stage batch testing. Also, it was found that the model is more efficient than the classical two-stage batch testing for relatively high values of proportion.

Run-to-run product quality control of batch processes

Batch processes have been increasingly used in the production of low volume and high value added products. Consequently, optimization control in batch processes is crucial in order to derive the maximum benefit. In this paper, a run-to-run product quality control based on iterative learning optimization control is developed. Moreover, a rigorous theorem is proposed and proven in this paper, which states that the tracking error under the optimal iterative learning control （ILC） law can converge to zero. In this paper, a typical nonlinear batch continuous stirred tank reactor （CSTR） is considered, and the results show that the performance of trajectory tracking is gradually improved by the ILC.

Predictive Sliding Mode Control to a Thermal Process for Batch Dyeing

The batch dyeing process is a typical nonlinear process with time-delay,where precise controlling of temperature plays a vital role on the dyeing quality.Because the accuracy and robustness of the commonly used proportion integration differentiation(PID) algorithm had been limited,a novel method was developed to precisely control the heating and cooling stages for batch dyeing process based on predictive sliding mode control(SMC) algorithm.Firstly,a special predictive sliding mode model was constructed according to the principle of generalized predictive control(GPC);secondly,an appropriate reference trajectory for SMC was designed based on the improved approaching law;finally,the predictive sliding mode model and the Diophantine equation were used to predict the output and then the optimized control law was derived using the generalized predictive law.This method combined GPC and the SMC with their respective advantages,so it could be applied to time-delay process,making the control system more robust.Simulation experiments show that this algorithm can well track the temperature variation for the batch dyeing process.

Model-Based Split-Range Algorithm for the Temperature Control of a Batch Reactor

In the manufacturing processes of high value-added products in the pharmaceutical, fine chemical polymer and food industry, insufficient control might produce off-grade products. This can cause significant financial losses, or in the pharmaceutical industry, it can result in an unusable batch. In these industries, batch reactors are commonly used, the control of which is essentially a problem of temperature control. In the industry, an increasing number of heating-cooling systems utilising three different temperature levels can be found, which are advantageous from an economic point of view. However, it makes the control more complicated. This paper presents a split-range designing technique using the model of the controlled system with the aim to design a split-range algorithm more specific to the actual sys- tem. The algorithm described provides high control performance when using it with classical PID-based cascade temperature control of jacketed batch reactors;however, it can be used with or as part of other types of controllers, for ex- ample, model-based temperature controllers. The algorithm can be used in the case of systems where only two as well as where three temperature levels are used for temperature control. Besides the switching between the modes of opera- tion and calculating the value of the manipulated variable, one of the most important functions of the split-range algo- rithm is to keep the sign of the gain of the controlled system unchanged. However, with a more system-specific split-range solution, not only can the sign of the gain be kept unchanged, but the gain can also be constant or less de- pendent on the state of the system. Using this solution, the design of the PID controller becomes simpler and can be implemented in existing systems without serious changes.

面向云存储的属性基双边访问控制方案

针对目前云存储中细粒度双边访问控制机制安全模型较弱且外包解密结果缺乏验证的问题,提出了一种面向云存储数据的属性基双边访问控制方案。首先,提出了自适应安全可验证外包双边CP-ABE的形式化定义和安全模型;其次,以此为基础并结合批量可验证技术在合数阶群上设计了双边访问控制方案,支持数据拥有者与数据使用者同时为对方定义访问策略;最后,安全性分析表明,所提方案在自适应安全模型下针对选择明文攻击与选择消息攻击是不可区分的和存在性不可伪造的。实验结果显示,所提方案减轻了用户端的匹配、解密以及验证阶段的计算开销。

批生产条件下白酒质量控制探讨

食品安全管理要求企业构建可追溯体系,批次管理是追溯体系的重要内容,目前白酒从原料至成品的批次划分尚没有具体的法规要求,因此白酒如何定义生产全过程批次尤为关键。本文在分析白酒酿造、灌装等核心环节的基础上,设计了原料至灌装的批次编码,建立了可追溯流程,探讨了白酒批次管理中存在的问题并提出建议,以期为企业批次管理的制定提供参考。

使用试剂盒内自带质控品的室内质量控制管理改进措施的研究

目的探讨一种基于试剂盒内自带质控品条件下的稳定有效的质控管理措施。方法选取中国人民解放军联勤保障部队第九〇七医院检验科2022年11月2日—2023年7月22日室内质控数据进行分析。更换试剂批次时试剂盒内质控品批次也同时更换,观察试剂盒内自带质控品和第三方质控品室内质控Levey-Jennings图变化,评估各批次允许不精密度[以变异系数(coefficient of variation,CV)表示]。采用固定试剂盒内自带质控品批次,更换试剂批次时试剂自带质控品批次不更换,观察试剂盒内自带质控品和第三方质控品Levey-Jennings图变化,评估各批次和累积允许不精密度(以CV表示),比较σ值差异。结果同时更换试剂及自带质控品共3个批次,虽然自带质控品和第三方质控品各批次CV均符合实验室质量控制目标CV≤10%。但是每次更换批号,第三方质控Levey-Jennings图出现明显批间差,用试剂自带质控品Levey-Jennings图则批内稳定,无法提示试剂批间差。采用固定试剂盒内自带质控品批次,更换试剂批次时试剂盒内自带质控品批次不更换的情况下,更换3个批次试剂,试剂盒内自带质控品和第三方质控品一样每次更换试剂批次Levey-Jennings图都出现较大差异,且自带质控品和第三方质控品各批次和累积CV均符合实验室质量控制目标CV≤10%,σ值均达到世界一流水平。试剂盒内自带质控品也可以反映试剂批间差,与第三方质控品达到一样的效果。结论通过改进试剂盒内自带质控品的使用管理措施,可以优化质控效率,降低临床安全隐患。

批式流加发酵中的鲁棒脉冲时滞最优控制

本文研究了批式流加发酵中的鲁棒脉冲时滞最优控制问题。首先,提出一个非线性状态依赖的脉冲时滞系统描述批式流加发酵甘油生产1,3-丙二醇(1,3-PD)过程。由于批式流加发酵过程中的动力学参数难以准确估计,本文建立了一个具有连续状态不等式约束的鲁棒脉冲时滞最优控制模型。这里,目标函数为终端时刻1,3-PD浓度及其关于动力学参数的灵敏性的加权和,控制向量为流加发生时甘油的临界浓度及每次流加时甘油的流加体积。然后,通过引入辅助脉冲系统,将该鲁棒最优控制问题转化为等价的标准最优控制问题。进一步,通过约束转换技术,将等价的最优控制问题转化为仅具有盒式约束的罚问题。最后,设计了一种并行差分进化算法求解转化后的罚问题。数值结果表明:当参数受到微小扰动时,尽管牺牲了少量的1,3-PD浓度,但是明显提高了系统的鲁棒性。

小卫星批生产装配一致性控制方法

针对微小卫星批量化生产制造过程,分析了批生产质量管理特点和每组星之间的装配质量一致性影响因素.从工程实践的角度,提出了一种适于卫星系统批生产的装配一致性控制方法.该方法通过建立规范化体系、质量重心前移、提高工艺方法稳定性、固化技术状态、装配流程标准化、实施质量确认制、实行"去型号化"物资管理、引进自动化设备及统筹人员配置等质量控制手段,保证了小卫星批量化生产过程中装配工作的质量一致性.将其应用到卫星批产总装工作后,实践结果表明,卫星装配一致性控制方法能够有效提升批量化生产制造工作质量,大幅提升批次卫星产品的质量一致性,是卫星批量生产研制质量的有力保证.

模型预测控制技术在婴配乳粉配料优化中应用

为降低婴幼儿配方乳粉生产过程中的营养成分含量波动,本研究基于模型预测控制(MPC)算法和序列二次规划(SQP)算法构建婴幼儿配方乳粉配料优化控制模型,进行配料过程营养成分含量预测、反馈校正和滚动优化。结果表明,通过配料优化控制模型模拟干预100批次乳粉生产过程后,蛋白质、脂肪和碳水化合物含量的标准差分别从5.18、4.91、5.86 g·kg^(-1)降低至1.01、1.22和1.33 g·kg^(-1),验证了配料优化控制模型的有效性。本研究可为实现婴幼儿配方乳粉的配料优化控制提供参考。

批产软件的研制流程和质量保证方法

在目前国内外承研业务发展和竞争形势下,越来越多的单机产品打破了零星或单件制造的生产模式,走上了产品化、批量化生产的道路。因此,软件的研制也需要从先前耗费大量人力与时间成本的模式转型为高效批量的模式。基于日常批产软件开发过程中的研制经验,文章介绍了批产软件概念、研制要求和流程、配置管理等,分析了批产软件研制过程中的流程和质量保证方法,提出了一些操作建议,旨在助推批产软件研制和质量保证工作的优化。

面向批产化遥感卫星的热控系统研制优化与实践

文章针对批产化遥感卫星研制要求,探索了热控系统的优化策略,应用于“吉林一号”某批产卫星的研制过程。相较单星侧重于目标可行性验证的研制流程,批产卫星的研制重点为更低成本、更高效率的生产。文章遵循结构-热控一体化的思路,进行了低外热流敏感性、低控温功耗、低材料成本的热控设计;依托脉动式AIT生产线及自动化生产设备,细化热控实施流程,缩短准备工作时长,灵活安排人员工位,大幅提升卫星生产效率;依据批产卫星的热控设计特点及试验需求,采用基于等效热沉的外热流模拟方法,降低了真空热试验的复杂性。当前该型号批产遥感卫星已生产并发射54颗,卫星在轨状态良好,该热控批产化研制思路可为其他批产卫星的研制提供有效参考。

降低间歇法聚丙烯灰分的控制措施

间歇式液相本体法聚丙烯工艺是20世纪70年代我国自行开发的,该工艺具有流程短、设备简单、投资小、见效快的特点。但是随着人们对聚丙烯质量和数量要求的不断提升(该工艺同时存在着产品质量不稳定、品种单一、成本偏高的缺点),特种牌号聚丙烯成为当下市场紧缺的产品,其中超低灰聚丙烯成为人们越来越关注的焦点,考验着聚合釜的操作灵活性。本文介绍了降低间歇法聚丙烯灰分的控制措施。

双机架宽厚板轧机轧制效率提升措施及实践

针对莱钢4 300 mm粗轧机、精轧机间距离短对轧制节奏影响大的现状,通过辊道分组、辊道逻辑控制优化、缩短待温板坯间距等措施,最大限度增加了待温辊道的长度和批轧块数;通过优化辊道接钢、分钢优先权判断功能的开发、精轧机咬钢顺控优化等措施,减少了时间浪费;通过跟踪物料影像保持功能减少了物料跟踪错误导致的停机时间。以上措施的实施,使产线生产效率提升15%以上。